Implementation of an Automated Learning System for Non-experts
This addresses the need for industries to adopt AI by providing a user-friendly tool, though it is incremental as it builds on existing automated machine learning concepts.
The paper tackled the problem of making machine learning accessible to non-experts by implementing an automated system called YMIR, which allows users to label data, train models, and perform evaluations through a graphical interface without AI knowledge, with the code released on GitHub.
Automated machine learning systems for non-experts could be critical for industries to adopt artificial intelligence to their own applications. This paper detailed the engineering system implementation of an automated machine learning system called YMIR, which completely relies on graphical interface to interact with users. After importing training/validation data into the system, a user without AI knowledge can label the data, train models, perform data mining and evaluation by simply clicking buttons. The paper described: 1) Open implementation of model training and inference through docker containers. 2) Implementation of task and resource management. 3) Integration of Labeling software. 4) Implementation of HCI (Human Computer Interaction) with a rebuilt collaborative development paradigm. We also provide subsequent case study on training models with the system. We hope this paper can facilitate the prosperity of our automated machine learning community from industry application perspective. The code of the system has already been released to GitHub (https://github.com/industryessentials/ymir).